(1) Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, 77030, TX
Abstract:
A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.
